SYSTEM AND METHOD FOR SCAN TIME REDUCTION FOR PROPELLER MAGNETIC RESONANCE IMAGING ACQUISITION USING DEEP LEARNING RECONSTRUCTION

    公开(公告)号:US20250157098A1

    公开(公告)日:2025-05-15

    申请号:US18506457

    申请日:2023-11-10

    Abstract: A system and method for reducing scan time of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) imaging include acquiring a plurality of blades of k-space data of a region of interest in a rotational manner around a center of k-space via a magnetic resonance imaging (MRI) scanner during a PROPELLER sequence, wherein each blade of the plurality of blades of k-space data includes a plurality of parallel phase encoding lines sampled in a phase encoding order. Each blade of the plurality of blades of k-space data is undersampled. The system and method include utilizing a deep learning-based Cartesian-like reconstruction network to individually and separately reconstruct each blade of the plurality of blades of k-space data to generate a plurality of fully sampled blades. The system and method include utilizing a PROPELLER reconstruction algorithm to generate a complex image from the plurality of fully sampled blades.

    SYSTEM AND METHODS FOR SEQUENTIAL SCAN PARAMETER SELECTION

    公开(公告)号:US20210174496A1

    公开(公告)日:2021-06-10

    申请号:US16703547

    申请日:2019-12-04

    Abstract: Methods and systems are provided for sequentially selecting scan parameter values for ultrasound imaging. In one example, a method includes selecting a first parameter value for the a first scan parameter based on an image quality of each ultrasound image of a first plurality of ultrasound images of an anatomical region, each ultrasound image of the first plurality of ultrasound images having a different parameter value for the first scan parameter, selecting a second parameter value for a second scan parameter based on an image quality of each ultrasound image of a second plurality of ultrasound images of the anatomical region, each ultrasound image of the second plurality of ultrasound images having a different parameter value for the second scan parameter, and applying the first parameter value for the first scan parameter and the second parameter value for the second scan parameter to one or more additional ultrasound images.

    System and methods for sequential scan parameter selection

    公开(公告)号:US11308609B2

    公开(公告)日:2022-04-19

    申请号:US16703547

    申请日:2019-12-04

    Abstract: Methods and systems are provided for sequentially selecting scan parameter values for ultrasound imaging. In one example, a method includes selecting a first parameter value for the a first scan parameter based on an image quality of each ultrasound image of a first plurality of ultrasound images of an anatomical region, each ultrasound image of the first plurality of ultrasound images having a different parameter value for the first scan parameter, selecting a second parameter value for a second scan parameter based on an image quality of each ultrasound image of a second plurality of ultrasound images of the anatomical region, each ultrasound image of the second plurality of ultrasound images having a different parameter value for the second scan parameter, and applying the first parameter value for the first scan parameter and the second parameter value for the second scan parameter to one or more additional ultrasound images.

    DISTILLATION OF DEEP ENSEMBLES
    7.
    发明公开

    公开(公告)号:US20240281649A1

    公开(公告)日:2024-08-22

    申请号:US18170888

    申请日:2023-02-17

    CPC classification number: G06N3/08 G06N5/04

    Abstract: Systems/techniques that facilitate improved distillation of deep ensembles are provided. In various embodiments, a system can access a deep learning ensemble configured to perform an inferencing task. In various aspects, the system can iteratively distill the deep learning ensemble into a smaller deep learning ensemble configured to perform the inferencing task, wherein a current distillation iteration can involve training a new neural network of the smaller deep learning ensemble via a loss function that is based on one or more neural networks of the smaller deep learning ensemble which were trained during one or more previous distillation iterations.

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